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Activity Number: 405
Type: Invited
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #318101
Title: On the Widespread and Critical Impact of Systematic Bias and Batch Effects in Single-Cell RNA-Seq Data
Author(s): Stephanie C. Hicks* and Mingxiang Teng and Rafael A. Irizarry
Companies: Dana-Farber Cancer Institute/Harvard T.H. Chan School of Public Health and Dana-Farber Cancer Institute/Harvard T.H. Chan School of Public Health and Dana-Farber Cancer Institute
Keywords: single-cell ; RNA-Seq ; batch effects ; high-throughput data
Abstract:

Single-cell RNA-Sequencing (scRNA-Seq) has become the most widely used high-throughput method for transcription profiling of individual cells. Systematic errors, including batch effects, have been widely reported as a major challenge in high-throughput technologies. Surprisingly, these issues have received minimal attention in published studies based on scRNA-Seq technology. We examined data from fifteen published studies and found that systematic errors can explain a substantial percentage of observed cell-to-cell expression variability. Specifically, we found that the proportion of genes reported as expressed explains a substantial part of observed variability and that this quantity varies systematically across experimental batches. Furthermore, we found that the implemented experimental designs confounded outcomes of interest with batch effects, a design that can bring into question some of the conclusions of these studies. Finally, we propose a simple experimental design that can ameliorate the effect of theses systematic errors have on downstream results.


Authors who are presenting talks have a * after their name.

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